Scalable Randomized Kernel Methods for Multiview Data Integration and Prediction

We develop scalable randomized kernel methods for jointly associating data from multiple sources and simultaneously predicting an outcome or classifying a unit into one of two or more classes. The proposed methods model nonlinear relationships in multiview data together with predicting a clinical outcome and are capable of identifying variables or groups of variables that best contribute to the relationships among the views. We use the idea that random Fourier bases can approximate shift-invariant kernel functions to construct nonlinear mappings of each view and we use these mappings and the outcome variable to learn view-independent low-dimensional representations. Through simulation studies, we show that the proposed methods outperform several other linear and nonlinear methods for multiview data integration. When the proposed methods were applied to gene expression, metabolomics, proteomics, and lipidomics data pertaining to COVID-19, we identified several molecular signatures forCOVID-19 status and severity. Results from our real data application and simulations with small sample sizes suggest that the proposed methods may be useful for small sample size problems. Availability: Our algorithms are implemented in Pytorch and interfaced in R and would be made available at: https://github.com/lasandrall/RandMVLearn.Comment: 24 pages, 5 figures, 4 table

Automatic purpose-driven basis set truncation for time-dependent Hartree–Fock and density-functional theory

Real-time time-dependent density-functional theory (RT-TDDFT) and linear response time-dependent density-functional theory (LR-TDDFT) are two important approaches to simulate electronic spectra. However, the basis sets used in such calculations are usually the ones designed mainly for electronic ground state calculations. In this work, we propose a systematic and robust scheme to truncate the atomic orbital (AO) basis set employed in TDDFT and TD Hartree–Fock (TDHF) calculations. The truncated bases are tested for both LR- and RT-TDDFT as well as RT-TDHF approaches, and provide an acceleration up to an order of magnitude while the shifts of excitation energies of interest are generally within 0.2 eV. The procedure only requires one extra RT calculation with 1% of the total propagation time and a simple modification on basis set file, which allows an instant application in any quantum chemistry package supporting RT-/LR-TDDFT calculations. Aside from the reduced computational effort, this approach also offers valuable insight into the effect of different basis functions on computed electronic excitations and further ideas on the design of basis sets for special purposes

Magnetic Interactions in a [Co(II)3Er(III)(OR)4] Model Cubane through Forefront Multiconfigurational Methods

Strong electron correlation effects are one of the major challenges in modern quantum chemistry. Polynuclear transition metal clusters are peculiar examples of systems featuring such forms of electron correlation. Multireference strategies, often based on but not limited to the concept of complete active space, are adopted to accurately account for strong electron correlation and to resolve their complex electronic structures. However, transition metal clusters already containing four magnetic centers with multiple unpaired electrons make conventional active space based strategies prohibitively expensive, due to their unfavorable scaling with the size of the active space. In this work, forefront techniques, such as density matrix renormalization group (DMRG), full configuration interaction quantum Monte Carlo (FCIQMC), and multiconfiguration pair-density functional theory (MCPDFT), are employed to overcome the computational limitation of conventional multireference approaches and to accurately investigate the magnetic interactions taking place in a [Co(II)3Er(III)(OR)4] (chemical formula [Co(II)3Er(III)(hmp)4(μ2-OAc)2(OH)3(H2O)], hmp = 2-(hydroxymethyl)-pyridine) model cubane water oxidation catalyst. Complete active spaces with up to 56 electrons in 56 orbitals have been constructed for the seven energetically lowest different spin states. Relative energies, local spin, and spin–spin correlation values are reported and provide crucial insights on the spin interactions for this model system, pivotal in the rationalization of the catalytic activity of this system in the water-splitting reaction. A ferromagnetic ground state is found with a very small, ∼50 cm–1, highest-to-lowest spin gap. Moreover, for the energetically lowest states, S = 3–6, the three Co(II) sites exhibit parallel aligned spins, and for the lower states, S = 0–2, two Co(II) sites retain strong parallel spin alignment

Interpretable Deep Learning Methods for Multiview Learning

Technological advances have enabled the generation of unique and complementary types of data or views (e.g. genomics, proteomics, metabolomics) and opened up a new era in multiview learning research with the potential to lead to new biomedical discoveries. We propose iDeepViewLearn (Interpretable Deep Learning Method for Multiview Learning) for learning nonlinear relationships in data from multiple views while achieving feature selection. iDeepViewLearn combines deep learning flexibility with the statistical benefits of data and knowledge-driven feature selection, giving interpretable results. Deep neural networks are used to learn view-independent low-dimensional embedding through an optimization problem that minimizes the difference between observed and reconstructed data, while imposing a regularization penalty on the reconstructed data. The normalized Laplacian of a graph is used to model bilateral relationships between variables in each view, therefore, encouraging selection of related variables. iDeepViewLearn is tested on simulated and two real-world data, including breast cancer-related gene expression and methylation data. iDeepViewLearn had competitive classification results and identified genes and CpG sites that differentiated between individuals who died from breast cancer and those who did not. The results of our real data application and simulations with small to moderate sample sizes suggest that iDeepViewLearn may be a useful method for small-sample-size problems compared to other deep learning methods for multiview learning

Syndromic Surveillance of Motor Vehicle Crash Related Injuries in Nebraska

Objective The objective of this pilot study is to demonstrate the value of emergency department (ED) syndromic surveillance (SS) data to aid the surveillance of motor vehicle crash (MVC) related injuries in Nebraska

Pricing decisions during the FIFA 2014 World Cup: São Paulo and Rio de Janeiro

Purpose: This report explores the host destination’s response to the 2014 FIFA World Cup. The study establishes how the hotel Key Performance Indicators (KPIs) of two of the main cities in Brazil reacted to the World Cup. Originality/value: Exploring Brazilian hotel revenue managers’ responses to a major sporting event in Latin America is the main contribution of this research report. Relevance of the topic: Several business sectors in the travel and hospitality industry experience distinctive positive and negative performance for hosting mega-sporting events. The findings of this research report will be helpful for hotel revenue managers who regularly propose strategic pricing strategies. This report examines the response to the 2014 FIFA World Cup which is determined by the variance in the hotel key performance indexes: occupancy, average daily rate (ADR), revenue per available room (RevPAR), and supply. Design/methodology/approach: Using data gathered from Smith Travel Research (STR), this research report distinctly displays how two of the main Brazilian host cities (São Paulo and Rio de Janeiro) reacted to the World Cup. The obtained hotel KPIs represented annual data three years before the event and year after the event. The STR data entailed monthly hotel-level performance information, rooms revenue and rooms sold (demand) from the period of 2011-2015 containing a broad sample of Brazilian hotels in São Paulo and Rio de Janeiro. Key findings: Results of the analysis for São Paulo suggest that supply level did not increase drastically during the sporting event. However, ADR levels increased significantly during the event. Additionally, the variances in occupancy in 2014 echo with the variances in ADR in the same time, showing the elasticity of price. Results of the analysis for Rio de Janeiro suggest that supply volumes stay consistent before, during, and after the sporting event. The findings suggest that ADR and RevPAR were quite related, with notable increases and declines before and after the event during the comparable months in previous years. Implications for practice and policy: Our findings contained expected and unexpected results for hotel managers. For instance, Rio de Janeiro experienced growth of hotel room supply during the mega event. For this city, the drastic increase in ADR and supply did not result in a sharp decrease of their hotel occupancy rate. Other results suggest that São Paulo did not experience any changes in their hotel room supply level during the event. The performance of the occupancy rate still shows differences. For São Paulo, although there is no supply increase of the hotel industry, the occupancy rate still dropped during the mega event. This report lends support to the theory of price inelasticity of demand during mega sporting events, such as the FIFA World Cup

Mutational analyses of the signals involved in the subcellular location of DSCR1

BACKGROUND: Down syndrome is the most frequent genetic disorder in humans. Rare cases involving partial trisomy of chromosome 21 allowed a small chromosomal region common to all carriers, called Down Syndrome Critical Region (DSCR), to be determined. The DSCR1 gene was identified in this region and is expressed preferentially in the brain, heart and skeletal muscle. Recent studies have shown that DSCR1 belongs to a family of proteins that binds and inhibits calcineurin, a serine-threonine phosphatase. The work reported on herein consisted of a study of the subcellular location of DSCR1 and DSCR1-mutated forms by fusion with a green fluorescent protein, using various cell lines, including human. RESULTS: The protein's location was preferentially nuclear, independently of the isoform, cell line and insertion in the GFP's N- or C-terminal. A segment in the C-terminal, which is important in the location of the protein, was identified by deletion. On the other hand, site-directed mutational analyses have indicated the involvement of some serine and threonine residues in this event. CONCLUSION: In this paper, we discuss the identification of amino acids which can be important for subcellular location of DSCR1. The involvement of residues that are prone to phosphorylation suggests that the location and function of DSCR1 may be regulated by kinases and/or phosphatases

Hepatic Stellate Cell Senescence in Liver Fibrosis:Characteristics, Mechanisms and Perspectives

Myofibroblasts play an important role in fibrogenesis. Hepatic stellate cells are the main precursors of myofibroblasts. Cellular senescence is the terminal cell fate in which proliferating cells undergo irreversible cell cycle arrest. Senescent hepatic stellate cells were identified in liver fibrosis. Senescent hepatic stellate cells display decreased collagen production and proliferation. Therefore, induction of senescence could be a protective mechanism against progression of liver fibrosis and the concept of therapy-induced senescence has been proposed to treat liver fibrosis. In this review, characteristics of senescent hepatic stellate cells and the essential signaling pathways involved in senescence are reviewed. Furthermore, the potential impact of senescent hepatic stellate cells on other liver cell types are discussed. Senescent cells are cleared by the immune system. The persistence of senescent cells can remodel the microenvironment and interact with inflammatory cells to induce aging-related dysfunction. Therefore, senolytics, a class of compounds that selectively induce death of senescent cells, were introduced as treatment to remove senescent cells and consequently decrease the disadvantageous effects of persisting senescent cells. The effects of senescent hepatic stellate cells in liver fibrosis need further investigation

The Social-Safety System: Fortifying Relationships in the Face of the Unforeseeable

A model of the social-safety system is proposed to explain how people sustain a sense of safety in the relational world when they are not able to foresee the behavior of others. In this model, people can escape the acute anxiety posed by agents in their personal relational world behaving unexpectedly (e.g., spouse, child) by defensively imposing well-intentioned motivations on the agents controlling their sociopolitical relational world (e.g., President, Congress). Conversely, people can escape the acute anxiety posed by sociopolitical agents behaving unexpectedly by defensively imposing well-intentioned motivations on the agents controlling their personal relational world. Two daily diary studies, a longitudinal study of the 2018 midterm election, and a 3-year longitudinal study of newlyweds supported the hypotheses. On a daily basis, people who were less certain they could trust their romantic partner defended against acutely unforeseeable behavior in one relational world by affirming faith in the well-intentioned motivations of agents in the alternate world. Moreover, when people were more in the personal daily habit of finding safety in the alternate relational world in the face of the unexpected, those who were initially uncertain they could trust their romantic partner later evidenced greater comfort depending on their personal relationship partners